结核与肺部疾病杂志 ›› 2022, Vol. 3 ›› Issue (3): 203-208.doi: 10.19983/j.issn.2096-8493.20220016

• 论著 • 上一篇    下一篇

慢性阻塞性肺疾病患者症状群及其亚组识别的研究

徐艳松1, 刘庆华2, 邱筱婷1()   

  1. 1山东省枣庄市胸科医院护理部,滕州 277500
    2山东省枣庄市胸科医院呼吸科,滕州 277500
  • 收稿日期:2022-02-14 出版日期:2022-06-20 发布日期:2022-06-15
  • 通信作者: 邱筱婷 E-mail:2758776114@qq.com

Study on the identification of chronic obstructive pulmonary disease symptom clusters and their subgroups

XU Yan-song1, LIU Qing-hua2, QIU Xiao-ting1()   

  1. 1Nursing Department of Zaozhuang Tumour Hospital,Shandong Province,Tengzhou 277500,China
    2Respiratory Department of Zaozhuang Tumour Hospital,Shandong Province,Tengzhou 277500,China
  • Received:2022-02-14 Online:2022-06-20 Published:2022-06-15
  • Contact: QIU Xiao-ting E-mail:2758776114@qq.com

摘要:

目的:根据慢性阻塞性肺疾病(简称“慢阻肺”)患者症状发生率,确定症状群组成;基于患者症状群内症状体验强度的不同,鉴别症状群亚组,为临床制定个性化管理策略提供思路。方法:选取2020年8月至2021年8月165例在山东省枣庄市胸科医院住院的慢阻肺患者,采用一般资料调查表、记忆症状评定量表对患者症状严重程度进行调查。通过对患者发生率≥30%的症状进行探索性因子分析,分析症状群的组成,并基于所得症状群的得分差异使用系统聚类分析识别各症状群的亚组。采用方便抽样法,共发放165份问卷,均有效收回,有效率为100.00%。结果:165例慢阻肺住院患者发生频率最高的5个症状分别为气促(87.27%,144/165)、咳嗽(84.85%,140/165)、精力不足(57.58%,95/165)、口干(51.52%,85/165)、感到昏昏欲睡/易困(46.06%,76/165)。因子分析共提取3个症状群:情感症状群、咳嗽-疲乏症状群、疼痛-气促症状群,累积方差贡献率为56.34%;聚类分析结果显示3个症状群患者症状体验均存在差异,每一个症状群均识别出2个症状群亚组。结论:慢阻肺患者症状经历复杂,且症状之间存在相关性并以症状群形式存在,患者对群内各症状的体验强度不同,可以此划分为不同症状群亚组。识别症状群并鉴别不同症状群亚组,为医护人员全面评估病情,预见性地制定个体化干预措施提供依据,从而更大程度地促进患者康复。

关键词: 肺疾病,慢性阻塞性, 体征和症状, 呼吸系统, 聚类分析

Abstract:

Objective: To determine the composition of symptom clusters according to the incidence of different symptoms in chronic obstructive pulmonary disease (COPD) patients. Based on the intensity of experiences for different symptoms in patients within symptom clusters, we can identify the subgroups of symptom clusters and provide ideas for developing personalized clinical management strategies. Methods: A total of 165 COPD patients hospitalized in Zaozhuang Tumour Hospital of Shandong Province from August 2020 to August 2021 were selected. General data questionnaire and Memory symptom rating scale were used to investigate the severity of symptoms. Exploratory factor analysis was performed to analyze the composition of symptom clusters based on symptoms with incidences of 30% or more, and systematic cluster analysis was used to identify subgroups of each symptom cluster based on the score of the obtained symptom clusters. A total of 165 questionnaires were issued with convenience sampling method, and all of them were effectively retrieved. Results: The five most common symptoms in hospitalized COPD patients were shortness of breath (87.27%, 144/165), cough (84.85%,140/165), anenergia (57.58%,95/165), dry mouth (51.52%,85/165), drowsy (46.06%,76/165). Factor analysis identified three symptom clusters: affective symptom cluster, cough-fatigue symptom cluster and pain-shortness of breath symptom cluster, and the cumulative variance contribution rate was 56.34%. Cluster analysis showed that there were differences in symptom experiences among patients in the three symptom clusters, and two symptom sub-groups were identified for each symptom cluster. Conclusion: COPD patients experience complex symptoms that are correlated and present as clusters of symptoms. Patients experience different intensities of symptoms in the cluster, which can be divided into subgroups for symptom clusters. The identification of symptom clusters and their subgroups can provide a basis for medical staff to comprehensively evaluate the condition and formulate individualized intervention measures in advance, thus promote the recovery of patients to a greater extent.

Key words: Pulmonary disease,chronic obstructive, Signs and symptoms, Respiratory, Cluster analysis

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